






"""simple docstring"""
import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    BioGptForCausalLM,
			    BioGptForSequenceClassification,
			    BioGptForTokenClassification,
			    BioGptModel,
			    BioGptTokenizer,
			)
			from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST







class      UpperCamelCase_			:





	def __init__( self   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[int]=13      ,      lowerCAmelCase_   :					List[str]=7      ,      lowerCAmelCase_   :					List[str]=True      ,      lowerCAmelCase_   :					Any=True      ,      lowerCAmelCase_   :					List[str]=False      ,      lowerCAmelCase_   :					int=True      ,      lowerCAmelCase_   :					str=99      ,      lowerCAmelCase_   :					Union[str, Any]=32      ,      lowerCAmelCase_   :					Dict=5      ,      lowerCAmelCase_   :					Optional[Any]=4      ,      lowerCAmelCase_   :					Dict=37      ,      lowerCAmelCase_   :					Tuple="gelu"      ,      lowerCAmelCase_   :					Optional[Any]=0.1      ,      lowerCAmelCase_   :					int=0.1      ,      lowerCAmelCase_   :					int=512      ,      lowerCAmelCase_   :					int=16      ,      lowerCAmelCase_   :					List[str]=2      ,      lowerCAmelCase_   :					Dict=0.0_2      ,      lowerCAmelCase_   :					str=3      ,      lowerCAmelCase_   :					int=4      ,      lowerCAmelCase_   :					List[str]=None      ,      )		->     Union[str, Any]:
				UpperCAmelCase_	:					List[Any]     	=   parent
				UpperCAmelCase_	:					Tuple     	=   batch_size
				UpperCAmelCase_	:					List[str]     	=   seq_length
				UpperCAmelCase_	:					Union[str, Any]     	=   is_training
				UpperCAmelCase_	:					Optional[int]     	=   use_input_mask
				UpperCAmelCase_	:					Union[str, Any]     	=   use_token_type_ids
				UpperCAmelCase_	:					List[str]     	=   use_labels
				UpperCAmelCase_	:					List[Any]     	=   vocab_size
				UpperCAmelCase_	:					Optional[Any]     	=   hidden_size
				UpperCAmelCase_	:					Optional[Any]     	=   num_hidden_layers
				UpperCAmelCase_	:					Any     	=   num_attention_heads
				UpperCAmelCase_	:					str     	=   intermediate_size
				UpperCAmelCase_	:					Union[str, Any]     	=   hidden_act
				UpperCAmelCase_	:					List[str]     	=   hidden_dropout_prob
				UpperCAmelCase_	:					Union[str, Any]     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					Any     	=   max_position_embeddings
				UpperCAmelCase_	:					Optional[Any]     	=   type_vocab_size
				UpperCAmelCase_	:					Union[str, Any]     	=   type_sequence_label_size
				UpperCAmelCase_	:					Dict     	=   initializer_range
				UpperCAmelCase_	:					Optional[int]     	=   num_labels
				UpperCAmelCase_	:					Any     	=   num_choices
				UpperCAmelCase_	:					List[str]     	=   scope





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[Any]:
				UpperCAmelCase_	:					Any     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size		)

				UpperCAmelCase_	:					Any     	=   None
				if self.use_input_mask:
							UpperCAmelCase_	:					Any     	=   random_attention_mask([self.batch_size, self.seq_length]		)

				UpperCAmelCase_	:					Optional[Any]     	=   None
				if self.use_token_type_ids:
							UpperCAmelCase_	:					str     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.type_vocab_size		)

				UpperCAmelCase_	:					List[Any]     	=   None
				UpperCAmelCase_	:					Any     	=   None
				UpperCAmelCase_	:					str     	=   None
				if self.use_labels:
							UpperCAmelCase_	:					int     	=   ids_tensor([self.batch_size]      ,      self.type_sequence_label_size		)
							UpperCAmelCase_	:					int     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.num_labels		)
							UpperCAmelCase_	:					Any     	=   ids_tensor([self.batch_size]      ,      self.num_choices		)

				UpperCAmelCase_	:					List[Any]     	=   self.get_config()

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     int:
				return BioGptConfig(
				    vocab_size=self.vocab_size      ,      hidden_size=self.hidden_size      ,      num_hidden_layers=self.num_hidden_layers      ,      num_attention_heads=self.num_attention_heads      ,      intermediate_size=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      type_vocab_size=self.type_vocab_size      ,      is_decoder=lowerCAmelCase_      ,      initializer_range=self.initializer_range      ,      )





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					Optional[Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					List[Any]     	=   BioGptModel(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					Union[str, Any]     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   model(lowerCAmelCase_		)
				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Any      ,      )		->     Optional[int]:
				UpperCAmelCase_	:					Any     	=   BioGptForCausalLM(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					Dict     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      token_type_ids=lowerCAmelCase_      ,      labels=lowerCAmelCase_		)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Any      ,      *lowerCAmelCase_   :					List[str]		)		->     Tuple:
				UpperCAmelCase_	:					Optional[int]     	=   BioGptModel(config=lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()

				# create attention mask
				UpperCAmelCase_	:					int     	=   torch.ones(input_ids.shape      ,      dtype=torch.long      ,      device=lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   self.seq_length // 2
				UpperCAmelCase_	:					str     	=   0

				# first forward pass
				UpperCAmelCase_	,      UpperCAmelCase_	:					List[Any]     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		).to_tuple()

				# create hypothetical next token and extent to next_input_ids
				UpperCAmelCase_	:					int     	=   ids_tensor((self.batch_size, 1)      ,      config.vocab_size		)

				# change a random masked slice from input_ids
				UpperCAmelCase_	:					Union[str, Any]     	=   ids_tensor((1,)      ,      lowerCAmelCase_		).item() + 1
				UpperCAmelCase_	:					Optional[int]     	=   ids_tensor((self.batch_size, 1)      ,      config.vocab_size		).squeeze(-1		)
				UpperCAmelCase_	:					Tuple     	=   random_other_next_tokens

				# append to next input_ids and attn_mask
				UpperCAmelCase_	:					Tuple     	=   torch.cat([input_ids, next_tokens]      ,      dim=-1		)
				UpperCAmelCase_	:					List[str]     	=   torch.cat(
				    [attn_mask, torch.ones((attn_mask.shape[0], 1)      ,      dtype=torch.long      ,      device=lowerCAmelCase_		)]      ,      dim=1      ,      )

				# get two different outputs
				UpperCAmelCase_	:					Tuple     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_	:					Optional[Any]     	=   model(lowerCAmelCase_      ,      past_key_values=lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)["last_hidden_state"]

				# select random slice
				UpperCAmelCase_	:					List[str]     	=   ids_tensor((1,)      ,      output_from_past.shape[-1]		).item()
				UpperCAmelCase_	:					str     	=   output_from_no_past[:, -1, random_slice_idx].detach()
				UpperCAmelCase_	:					int     	=   output_from_past[:, 0, random_slice_idx].detach()

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(lowerCAmelCase_      ,      lowerCAmelCase_      ,      atol=1e-3		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[int]      ,      *lowerCAmelCase_   :					Union[str, Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					Union[str, Any]     	=   BioGptModel(config=lowerCAmelCase_		).to(lowerCAmelCase_		).eval()

				UpperCAmelCase_	:					Union[str, Any]     	=   torch.ones(input_ids.shape      ,      dtype=torch.long      ,      device=lowerCAmelCase_		)

				# first forward pass
				UpperCAmelCase_	:					List[str]     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      use_cache=lowerCAmelCase_		)

				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   outputs.to_tuple()

				# create hypothetical multiple next token and extent to next_input_ids
				UpperCAmelCase_	:					Any     	=   ids_tensor((self.batch_size, 3)      ,      config.vocab_size		)
				UpperCAmelCase_	:					str     	=   ids_tensor((self.batch_size, 3)      ,      2		)

				# append to next input_ids and
				UpperCAmelCase_	:					Optional[Any]     	=   torch.cat([input_ids, next_tokens]      ,      dim=-1		)
				UpperCAmelCase_	:					Tuple     	=   torch.cat([attention_mask, next_attn_mask]      ,      dim=-1		)

				UpperCAmelCase_	:					Dict     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_	:					Optional[Any]     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      past_key_values=lowerCAmelCase_		)[
				    "last_hidden_state"
				]

				# select random slice
				UpperCAmelCase_	:					Dict     	=   ids_tensor((1,)      ,      output_from_past.shape[-1]		).item()
				UpperCAmelCase_	:					Union[str, Any]     	=   output_from_no_past[:, -3:, random_slice_idx].detach()
				UpperCAmelCase_	:					Any     	=   output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(lowerCAmelCase_      ,      lowerCAmelCase_      ,      atol=1e-3		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					str      ,      *lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					List[Any]=False		)		->     Dict:
				UpperCAmelCase_	:					int     	=   BioGptForCausalLM(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				if gradient_checkpointing:
							model.gradient_checkpointing_enable()

				UpperCAmelCase_	:					Optional[int]     	=   model(lowerCAmelCase_      ,      labels=lowerCAmelCase_		)
				self.parent.assertEqual(result.loss.shape      ,      ()		)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)		)
				result.loss.backward()





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					Tuple      ,      *lowerCAmelCase_   :					List[Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					str     	=   BioGptModel(lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers		)
				for key in model.state_dict().keys():
							if "c_proj" in key and "weight" in key:
										self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]		) - model_std		)      ,      0.0_0_1		)
										self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]		) - 0.0		)      ,      0.0_1		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Tuple      ,      *lowerCAmelCase_   :					Dict		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Tuple     	=   self.num_labels
				UpperCAmelCase_	:					int     	=   BioGptForTokenClassification(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					List[Any]     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      token_type_ids=lowerCAmelCase_		)
				self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.num_labels)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   self.prepare_config_and_inputs()
				(
				    (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      (
				    UpperCAmelCase_
				)	,      
				)	:					Dict     	=   config_and_inputs
				UpperCAmelCase_	:					List[Any]     	=   {"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict







@require_torch
class      UpperCamelCase_			(__A							,		__A							,		__A							,		unittest.TestCase	):
	__magic_name__       =							(
	    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
	    if is_torch_available()
	    else ()
	)
	__magic_name__       =							(BioGptForCausalLM,) if is_torch_available() else ()
	__magic_name__       =							(
	    {
	        '''feature-extraction''': BioGptModel,
	        '''text-classification''': BioGptForSequenceClassification,
	        '''text-generation''': BioGptForCausalLM,
	        '''token-classification''': BioGptForTokenClassification,
	        '''zero-shot''': BioGptForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	__magic_name__       =							False





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     List[str]:
				UpperCAmelCase_	:					Dict     	=   BioGptModelTester(self		)
				UpperCAmelCase_	:					Optional[Any]     	=   ConfigTester(self      ,      config_class=lowerCAmelCase_      ,      hidden_size=37		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Union[str, Any]:
				self.config_tester.run_common_tests()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[int]:
				UpperCAmelCase_	:					Dict     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     Dict:
				UpperCAmelCase_	:					Optional[Any]     	=   self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
							UpperCAmelCase_	:					Union[str, Any]     	=   type
							self.model_tester.create_and_check_model(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Any:
				UpperCAmelCase_	:					Dict     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Tuple:
				UpperCAmelCase_	:					Union[str, Any]     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase_      ,      gradient_checkpointing=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     List[Any]:
				UpperCAmelCase_	:					int     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Dict:
				UpperCAmelCase_	:					Dict     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[Any]:
				UpperCAmelCase_	:					int     	=   self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase_		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   BioGptForCausalLM.from_pretrained("microsoft/biogpt"		)
				model.to(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   BioGptTokenizer.from_pretrained("microsoft/biogpt"		)

				UpperCAmelCase_	:					Tuple     	=   "left"

				# Define PAD Token = EOS Token = 50256
				UpperCAmelCase_	:					Any     	=   tokenizer.eos_token
				UpperCAmelCase_	:					int     	=   model.config.eos_token_id

				# use different length sentences to test batching
				UpperCAmelCase_	:					Dict     	=   [
				    "Hello, my dog is a little",
				    "Today, I",
				]

				UpperCAmelCase_	:					Tuple     	=   tokenizer(lowerCAmelCase_      ,      return_tensors="pt"      ,      padding=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   inputs["input_ids"].to(lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   model.generate(
				    input_ids=lowerCAmelCase_      ,      attention_mask=inputs["attention_mask"].to(lowerCAmelCase_		)      ,      )

				UpperCAmelCase_	:					Dict     	=   tokenizer(sentences[0]      ,      return_tensors="pt"		).input_ids.to(lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   model.generate(input_ids=lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
				UpperCAmelCase_	:					List[Any]     	=   tokenizer(sentences[1]      ,      return_tensors="pt"		).input_ids.to(lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   model.generate(input_ids=lowerCAmelCase_      ,      max_length=model.config.max_length - num_paddings		)

				UpperCAmelCase_	:					Optional[int]     	=   tokenizer.batch_decode(lowerCAmelCase_      ,      skip_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.decode(output_non_padded[0]      ,      skip_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   tokenizer.decode(output_padded[0]      ,      skip_special_tokens=lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   [
				    "Hello, my dog is a little bit bigger than a little bit.",
				    "Today, I have a good idea of how to use the information",
				]
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      [non_padded_sentence, padded_sentence]		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[int]:
				for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_	:					str     	=   BioGptModel.from_pretrained(lowerCAmelCase_		)
							self.assertIsNotNone(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Any:
				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase_	:					Optional[Any]     	=   3
				UpperCAmelCase_	:					Optional[Any]     	=   input_dict["input_ids"]
				UpperCAmelCase_	:					int     	=   input_ids.ne(1		).to(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[int]     	=   ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size		)
				UpperCAmelCase_	:					Dict     	=   BioGptForSequenceClassification(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					str     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      labels=lowerCAmelCase_		)
				self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     str:
				UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase_	:					Dict     	=   3
				UpperCAmelCase_	:					Dict     	=   "multi_label_classification"
				UpperCAmelCase_	:					Dict     	=   input_dict["input_ids"]
				UpperCAmelCase_	:					Optional[int]     	=   input_ids.ne(1		).to(lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   ids_tensor(
				    [self.model_tester.batch_size, config.num_labels]      ,      self.model_tester.type_sequence_label_size		).to(torch.float		)
				UpperCAmelCase_	:					List[Any]     	=   BioGptForSequenceClassification(lowerCAmelCase_		)
				model.to(lowerCAmelCase_		)
				model.eval()
				UpperCAmelCase_	:					str     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_      ,      labels=lowerCAmelCase_		)
				self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)		)







@require_torch
class      UpperCamelCase_			(unittest.TestCase	):





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     List[Any]:
				UpperCAmelCase_	:					int     	=   BioGptForCausalLM.from_pretrained("microsoft/biogpt"		)
				UpperCAmelCase_	:					Union[str, Any]     	=   torch.tensor([[2, 4_805, 9, 656, 21]]		)
				UpperCAmelCase_	:					List[str]     	=   model(lowerCAmelCase_		)[0]

				UpperCAmelCase_	:					Union[str, Any]     	=   42_384

				UpperCAmelCase_	:					List[str]     	=   torch.Size((1, 5, vocab_size)		)
				self.assertEqual(output.shape      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   torch.tensor(
				    [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]]		)

				self.assertTrue(torch.allclose(output[:, :3, :3]      ,      lowerCAmelCase_      ,      atol=1e-4		)		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					List[Any]     	=   BioGptTokenizer.from_pretrained("microsoft/biogpt"		)
				UpperCAmelCase_	:					Tuple     	=   BioGptForCausalLM.from_pretrained("microsoft/biogpt"		)
				model.to(lowerCAmelCase_		)

				torch.manual_seed(0		)
				UpperCAmelCase_	:					List[str]     	=   tokenizer("COVID-19 is"      ,      return_tensors="pt"		).to(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   model.generate(
				    **lowerCAmelCase_      ,      min_length=100      ,      max_length=1_024      ,      num_beams=5      ,      early_stopping=lowerCAmelCase_      ,      )
				UpperCAmelCase_	:					List[Any]     	=   tokenizer.decode(output_ids[0]      ,      skip_special_tokens=lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   (
				    "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
				    " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
				    " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
				    " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
				    " more than 800,000 deaths."
				)
				self.assertEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase:   Dict					=		{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Optional[int]					=		[
         'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ViTMAEForPreTraining',
         'ViTMAELayer',
         'ViTMAEModel',
         'ViTMAEPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'TFViTMAEForPreTraining',
         'TFViTMAEModel',
         'TFViTMAEPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vit_mae import (
              VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
              ViTMAEForPreTraining,
              ViTMAELayer,
              ViTMAEModel,
              ViTMAEPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
     import sys

     lowerCAmelCase:   Tuple					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)